Çağrı Öner^1,* and Ertuğrul Çolak²

Oncologie, Vol.23, No.3, pp. 393-407, 2021, DOI:10.32604/Oncologie.2021.016958

Abstract piRNAs are novel members of small non-coding RNAs and have an impact on genetic and epigenetic mechanisms of cells. It was aimed to investigate the role of piR-651 on MCF-7 benign breast cancer cells by focusing on molecular characteristics. Anti-piR-651 was transfected and effects of piR-651 on proliferation, adhesion, and motility of MCF-7 cells were detected after the 24th, 48th, and 72nd hour. Gene expressions of piR-651, Ki-67, MMP-2, ERα, HIF-1α, and hTERT were determined by using RT-PCR. piR-651 inhibition caused the decrease of proliferation, adhesion (p < 0.001), and motility of MCF-7 cells. The efficiency of anti-piR-651 transfection supported… More >

Elda A. Flores-Contreras¹, Everardo González-González^2,3, Ana I. Zarazúa-Niño¹, Elsa N. Garza-Treviño¹, Natalia Martínez-Acuña¹, Viviana C. Zomosa-Signoret⁴, Román Vidaltamayo⁴, Gerardo E. Muñoz-Maldonado⁵, Raquel Garza-Guajardo⁶, Manuel de J. García-Solís⁷, Alejandro Abarca-Blanco³, Ana M. G. Rivas-Estilla¹, Carlos Córdova-Fletes^1,*

Oncologie, Vol.23, No.3, pp. 373-392, 2021, DOI:10.32604/oncologie.2021.017786

Abstract Breast cancer (BC) is one of the leading causes of death in women worldwide. A major challenge in BC is chemoresistance, which is often modulated by epigenetic regulators such as long non-coding RNAs (lncRNAs). Because these regulator lncRNAs may play diverse roles, determining their specific pathways and/or functions is crucial to identify possible biomarkers and/or therapeutic targets for BC. In this study, we used gene expression microarrays in order to identify lncRNAs related to the BC biology. We found, among six differentially expressed (DE) lncRNAs, that the expression of lnc-ERP44-3:6 was consistently down-regulated in all breast tumor tissues compared to… More >

Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi^*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1273-1286, 2022, DOI:10.32604/iasc.2022.020662

Abstract In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to combine segments together to identify… More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer… More >

Naglaa F. Soliman^1,2, Naglaa S. Ali², Mahmoud I. Aly^2,3, Abeer D. Algarni^1,*, Walid El-Shafai⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1315-1334, 2022, DOI:10.32604/cmc.2022.017001

Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the… More >

Hanan A. Hosni Mahmoud^*, Amal H. Alharbi, Doaa S. Khafga

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >

Faizah A. AlMalki¹, Aziza M. Hassan^2,*, Zeinab M. Klaab¹, Soliman Abdulla³, Antonio Pizzi⁴

Journal of Renewable Materials, Vol.9, No.12, pp. 2077-2092, 2021, DOI:10.32604/jrm.2021.016173

Abstract Recently, natural substances in the form of nanoparticles are increasingly being used in different field, particularly in medicines to enhance their beneficial effects in treatment and prevention. Cancer cells of the breast (MCF-7) have been chosen to be examined and treated in vitro with conventional drug Tamoxifen (Tam) and tannin nanoparticles extract (NP99) individually or in combination. MTT reagent has been applied to assess the cell viability and propagation percentage, DNA fragmentation and mRNA relative expression of apoptotic genes to study the cell death pathway. The results showed that Tam and tannin NP99 triggered cytotoxic activity towards the MCF-7 cell.… More >

Keiichi Kontani^1,*, Kana Kuraishi¹, Shin-ichiro Hashimoto¹, Shoko Norimura², Nozomi Hashimoto¹, Masahiro Ohtani³, Naomi Fujiwara-Honjo⁴, Manabu Date⁵, Koji Teramoto⁶, Hiroyasu Yokomise¹

Oncologie, Vol.23, No.2, pp. 229-239, 2021, DOI:10.32604/Oncologie.2021.016277

Abstract The prognosis of patients with human epidermal growth factor receptor-2 (HER2)-overexpressed metastatic breast cancer (MBC) has improved drastically following the development of anti-HER2 therapies. We question what factors are involved in the improved outcome by the treatment. One hundred and two MBC patients who received chemotherapy were classified into groups according to breast cancer subtype: luminal/HER2-negative (n = 50), HER2 (n = 26), and triple-negative subtypes (n = 26). Clinicopathologic features and clinical outcomes of the groups were compared. Disease-free intervals in the triple-negative group were significantly shorter than those in the other two groups. Age, tumor grade, the number… More >

Asma Baccouche^1,*, Begonya Garcia-Zapirain², Cristian Castillo Olea², Adel S. Elmaghraby¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1407-1425, 2021, DOI:10.32604/cmc.2021.018461

Abstract With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as… More >

Ornela Bardhi^1,2,*, Begonya Garcia Zapirain¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1595-1613, 2021, DOI:10.32604/cmc.2021.015326

Abstract Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178 features for BCa and 143… More >